The most carefully researched chart of Amazon product categories and the top 1%, 3%, 5%, and 10% Sales Ranks in each. Including an analysis of why existing Sales Rank charts are wrong.
What is an Amazon Sales Rank chart?
Sales Rank charts break down each Amazon product category into levels. They take the total number of products in each category, then identify the Sales Ranks that comprise the top 1%, 3% 5%, and 10% in each category.
And unfortunately, as I’ll show, these charts rely on inaccurate numbers and do more harm than good.
The calculation used for these charts are fairly simple: Take the total number of products in each category, then divide it by various numbers to get the top 1%, 5%, etc. For example, if a category had 10 million products in it, the top 1% would be any item ranked 100,000 or better.
Screenshot from Full Time FBA
Each product category has it’s own Sales Rank spectrum, so this figure must be calculated for every category to be useful. A product has a Sales Rank of 1 in Books and another product has a Sales Rank of 1 in DVDs, but no two books can have a Sales Rank of 1. Make sense?
But as I’ll explain, existing charts have problems. So I set out to create the most accurate Sales Rank chart there is. But first…
Who are these charts for?
The charts below fulfill two major uses.
Number one, to inform Amazon inventory buying decisions. You can’t know if you should sell an item without knowing it’s demand. These charts stratify products into simple demand levels. For example, the top 1% of products are a safe strata for most categories – you know products in the top 1% are selling frequently.
Number two, to inform pricing and repricing decisions. Like with sourcing, you can’t know how to price an item without knowing its demand. Repricing inventory in the top 1% will be an entirely different calculation than a product outside the top 10%.
Why Sales Rank charts can never be 100% accurate
- Amazon doesn’t reveal the number of products in each category
- The numbers are changing by the minute
The important detail to understand going forward is that Amazon data is messy, and you’re always dealing with estimates. The only question is: Which estimate is more accurate than others?
The problem with most Sales Rank charts
While there are tons of Amazon Sales Rank charts to be found online. Here are some examples:
When you look at them more closely, they have some issues.
(I’ve only looked at a few, and it’s possible there are charts out there that avoid the problems I’m about to outline).
How most charts calculate their figures
At a glance, it’s easy to see how these charts calculate the top 1%, 5%, etc selling products in each category. They simply take the total number of products, and multiply it by 0.01 (for 1%), 0.05 (for 5%), and so on.
That would make sense, but only if these two conditions apply:
- Every product in the category has sold at least once.
- The estimated number of products in each category is actually correct.
If either of these is wrong, it destroys the accuracy and utility of the entire chart. And as I’m about to show, both of these issues apply to every chart I’ve reviewed.
So we’re on the same page, make sure to understand these two reasons existing Sales Rank charts have accuracy problems:
- They rely on an inaccurate number of products in each category.
- They depend on the total number items in the category, not the total number that have sold.
Let’s explain each in more detail.
Problem #1: Relying on an inaccurate number of products in each category
At a glance, it’s easy to confirm the basis of the Sales Rank calculations in most charts is wrong. But how could they all be wrong? (Or at least the ones I reviewed?)
It appears they rely on Keepa’s “Category Tree” for their product category numbers. I could be wrong, but among every chart I’ve looked at, the numbers seem to match.
But most of the “number of products in the category” figures appear to be inaccurate, for reasons I’ll explain in a minute. That throws off the accuracy of the entire chart.
Problem #2: They look at total number of products, not total number that have sold
Read that last sentence again if you don’t understand. This is a huge difference.
Most product categories have many products that have never sold a single time. Most have millions of these products. Which means there are millions of products in most categories that have no Sales Rank.
So if you have a category with 100 million items, and half have never sold (this is roughly the numbers in the Books category), then saying a rank of 1 million is in the top 1% of all books is going to be totally wrong – because millions of those items don’t even have a Sales Rank. And I don’t mean just a little wrong – it’s going to be wrong by 100%. The real number should be 500,000.
The products in a category of never sold a single time, then they should be removed from the product category total. You calculate demand by looking at the total number of products that have sold, not the total number of products.
So if the product category totals figure can’t be relied upon, what can? The answer is the “worst rank in each category” figure, which Keepa’s Cateogry tree provides. But there are still a couple of problems…
The problem with Keepa’s “Category Tree”
Keepa is a great resources, and I’ve always called it the “gold standard” of Amazon data. But when it comes to it’s “Products in each category” estimates, the numbers are often questionable at best.
Keepa’s “Category Tree” does two main things:
- Estimates total number of products in each category.
- Shows the worst recorded Sales Rank in each category.
Sounds good. So how do we know it’s wrong?
The “worst rank” is often a higher number than the total number of products in the category.
Theoretically this should be impossible. If there are 100 million products in a category, the worst possible Sales Rank could be 100 million. Any number higher that that would be impossible.
Not only that, but any Sales Rank even close to 100 million would be impossible. Because no category has sales for every product in the category. It’s very common for a large percentage of products to never have sold. So I would be suspicious even if the worst rank was anywhere close to the total number of products.
So which figure is wrong: The product category numbers, or the Sales Rank numbers?
We can’t know, so the safest bet is to choose the lower number. Since they can’t both be right, assume the lower number is the more accurate one. This is the safest route to reliable data, and that should guide all estimates for any chart striving for accuracy.
An example of incorrect Sales Rank calculation
I’ve never been accused of having good manners, but I’m going to avoid calling out any particular chart here. And since most people reading this don’t rely on these charts in any meaningful way, you might think this doesn’t affect you.
What might affect you are the triggers used in scanning apps, some of which rely on incorrect Sales Rank percentage calculations. So here’s an example of a scouting app miscalculation that might hit closer to home.
Look at this:
This is a scan of my book, Reselling For Rebels. Let’s set aside the embarrassing reality that it’s just not selling that well (rank of 1.8 million). But focus on the right: “Top 1.89%.” Does anything seem off there?
If you weren’t versed in Amazon Sales Rank, you might be tempted to think “Peter Valley’s book is a best seller! Top 2% of all books on Amazon!” But the reality is, this calculation is simply wrong.
Saying a rank of 1.8 million is in the top 1.8% of all books on Amazon assumes the total number of books in Amazon’s catalog is exactly 100,000 million. And that’s simply not true. Two major issues:
- The number of books in Amazon’s catalog is probably closer to 50 million.
- The number of books that have actually sold (the only figure that matters) is closer to 25 million.
So while this app (not naming names) says my book is in the top 2% of books on Amazon, it’s actually more like 7%. Huge difference.
When you mess up the number of products in an Amazon category, and mess up the number of items in a rank, your percentage calculations are so hopelessly flawed as to be useless.
How I compiled the “most accurate Sales Rank chart”
Considering everything we just covered, I used a very simple formula for the chart that follows:
- I looked at the Keepa “Category Tree” for each category.
- I looked at the product category total and the “worst sales rank” total.
- I chose the the lowest number and used that as the basis for Sales Rank percentages.
This assumes if the “worst rank” figure is higher than the product category totals figure, then the Sales Rank figure is wrong. It also assumes if the product totals figure is higher than “worst rank,” the worst rank figure is wrong.
While no Amazon Sales Rank chart can be exact, this formula gives us the most accurate estimates possible.
Lastly, I rounded the numbers slightly for easier use in your repricer and scouting app. Since the numbers change wildly literally every day, exact precision isn’t important. Ease of use is.
With that out of the way, here’s the chart:
Top 1% Sales Rank chart for each major Amazon category
Books: 250,000
Movies & TV: 82,000
Music: 82,500
Toys & Games: 91,000
Appliances: 17,000
Arts/Crafts/Sewing: 170,000
Automotive: 300,000
Baby Products: 43,000
Beauty: 140,000
Cell Phones & Accessories: 170,000
Computers & Accessories: 140,000
Electronics: 270,000
Grocery: 37,000
Health/Personal Care: 100,000
Home & Kitchen: 310,000
Industrial/Scientific: 220,000
Musical Instruments: 30,000
Office Products: 130,000
Patio/Lawn/Garden: 240,000
Pet Supplies: 95,000
Software: 1,600
Sports & Outdoors: 310,000
Tools/Home Improvement: 310,000
Video Games: 50,000
Top 3% Sales Rank chart for each Amazon category
Books: 750,000
Movies & TV: 246,000
Music: 247,500
Toys & Games: 273,000
Appliances: 51,000
Arts/Crafts/Sewing: 510,000
Automotive: 900,000
Baby Products: 129,000
Beauty: 420,000
Cell Phones & Accessories: 510,000
Computers & Accessories: 420,000
Electronics: 810,000
Grocery: 111,000
Health/Personal Care: 300,000
Home & Kitchen: 930,000
Industrial/Scientific: 660,000
Musical Instruments: 90,000
Office Products: 390,000
Patio/Lawn/Garden: 720,000
Pet Supplies: 285,000
Software: 4,800
Sports & Outdoors: 930,000
Tools/Home Improvement: 930,000
Video Games: 150,000
Top 5% Sales Rank chart for each Amazon category
Books: 1,250,000
Movies & TV: 410,000
Music: 412,500
Toys & Games: 455,000
Appliances: 85,000
Arts/Crafts/Sewing: 850,000
Automotive: 1,500,000
Baby Products: 215,000
Beauty: 700,000
Cell Phones & Accessories: 850,000
Computers & Accessories: 700,000
Electronics: 1,350,000
Grocery: 185,000
Health/Personal Care: 500,000
Home & Kitchen: 1,550,000
Industrial/Scientific: 1,100,000
Musical Instruments: 150,000
Office Products: 650,000
Patio/Lawn/Garden: 1,200,000
Pet Supplies: 475,000
Software: 8,000
Sports & Outdoors: 1,550,000
Tools/Home Improvement: 1,550,000
Video Games: 250,000
Top 10% Sales Rank chart for each Amazon category
Books: 2,500,000
Movies & TV: 820,000
Music: 825,000
Toys & Games: 910,000
Appliances: 170,000
Arts/Crafts/Sewing: 1,700,000
Automotive: 3,000,000
Baby Products: 430,000
Beauty: 1,400,000
Cell Phones & Accessories: 1,700,000
Computers & Accessories: 1,400,000
Electronics: 2,700,000
Grocery: 370,000
Health/Personal Care: 1,000,000
Home & Kitchen: 3,100,000
Industrial/Scientific: 2,200,000
Musical Instruments: 300,000
Office Products: 1,300,000
Patio/Lawn/Garden: 2,400,000
Pet Supplies: 950,000
Software: 16,000
Sports & Outdoors: 3,100,000
Tools/Home Improvement: 3,100,000
Video Games: 500,000
You now have the data you need for sourcing and pricing
Plug these into your scouting app triggers. Use these in your repricer rules. And I’m confident this chart is more accurate than existing Sales Rank charts, and can better inform pricing and sourcing decisions.
-Peter Valley
I’m curious how we can apply this data into our repricing strategies. If 2.5K is top 10% in books, for example, what would time until next sale/how often the ASIN sells be? Would 2.5K on your chart still be around that 1-2 months until next sale timeframe?
Check out my article on understanding sales rank for books. Explains everything.